Speaker: Prof. Dr. Thomas Höllt (TU Delft)

High dimensional single-cell data is nowadays collected routinely for multiple applications in biology. Standard tools for the analysis of these data do not scale well with regard to the number of dimensions or the number of cells. To tackle these issues, we have extended and created new dimensionality reduction techniques such as A-tSNE[1] and HSNE[2,3]. We have implemented these in our integrated single-cell analysis framework Cytosplore and created new interaction methods such as CyteGuide[4] and Focus+Context for HSNE[5].

This presentation will give an overview over the Cytosplore Visual Analytics framework and highlight some of its domain applications.

[1]Approximated and User Steerable tSNE for Progressive Visual Analytics, IEEE Transactions on Visualization and Computer Graphics, 2017
[2] Hierarchical Stochastic Neighbor Embedding, Computer Graphics Forum (Proceedings of EuroVis 2016), 2016
[3] Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types, Nature Communications, 2017
[4] CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis, IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE InfoVis 2017), 2018
[5] Focus+Context Exploration of Hierarchical Embeddings, Computer Graphics Forum (Proceedings of EuroVis 2019), 2019

Assistant Professor for Visualization at Leiden Computational Biology Center at LUMC
Visiting Researcher at Computer Graphics and Visualization group at TU Delft




45 + 15
Host: Renata Raidou